Journal of Energy Bioscience 2024, Vol.15, No.6, 337-348 http://bioscipublisher.com/index.php/jeb 344 This technology can synchronously obtain chlorophyll fluorescence parameters (such as Fv/Fm, Φ PSII), canopy temperature, and hyperspectral reflectance, which are significantly correlated with physiological processes such as photosystem II quantum efficiency and non photochemical quenching. A high-throughput phenotype platform based on chlorophyll fluorescence imaging can achieve dynamic analysis of photosynthetic function from single leaf to population level, which is particularly suitable for studying the adaptive regulation mechanism of photosynthetic mechanisms under environmental fluctuations (Van Bezouw et al., 2019). By integrating remote sensing phenotype data with genome-wide association analysis (GWAS), QTL loci regulating photosynthetic efficiency can be accurately located, providing targets for molecular design breeding (Manning et al., 2023). This "phenotype genotype" collaborative analysis model significantly improves the genetic analysis efficiency of complex traits. 6.3 Potential of artificial intelligence in optimizing photosynthesis-related agronomic practices Artificial intelligence technology has shown significant potential for paradigm innovation in the field of photosynthesis and agronomic optimization. Its core advantage lies in integrating heterogeneous data from multiple sources (genome, phenotype group, environmental parameters, etc.), constructing predictive models through deep learning algorithms, and achieving intelligent agricultural decision-making. Taking the optimization of nitrogen use efficiency (NUE) in potatoes as an example, phenotype image analysis based on convolutional neural networks (CNN) combined with genome-wide association analysis (GWAS) can accurately identify key gene loci regulating nitrogen assimilation and transport (such as GS2, NRT2.1), providing molecular targets for variable fertilization strategies (Figure 3) (Tiwari et al., 2020). More importantly, the artificial intelligence system can dynamically adjust irrigation and fertilization plans by processing IoT sensor data (soil moisture, meteorological information, etc.) in real time, thereby increasing photosynthetically active radiation utilization efficiency (RUE) by 12%~15%. When coupled with chlorophyll fluorescence remote sensing monitoring and metabolic flux models, a closed-loop control system of "perception-decision-execution" can be constructed to achieve accurate optimization of potato population photosynthetic efficiency. This multidisciplinary research paradigm provides new ideas for breaking through the empirical limitations of traditional agronomy. 7 Future Directions and Research Gaps 7.1 Synergies between genetic and agronomic strategies The synergistic integration of genetic improvement and agronomic regulation is an important strategy to enhance the photosynthetic efficiency of potatoes. Metabolic flux analysis showed that by synergistically regulating the activity combination of key Calvin cycle enzymes such as Rubisco, FBP aldolase, and SBPase through multiple genes, the net photosynthetic rate can be increased by 28% (Vijayakumar et al., 2023). However, the implementation of these genetic gains relies on precise environmental regulation: variable fertilization techniques based on nitrogen dynamic monitoring can significantly improve the nitrogen use efficiency (NUE) of transgenic plants, thereby supporting their enhanced photosynthetic carbon assimilation potential (Tiwari et al., 2018; Wang et al., 2020). In addition, high-throughput phenotype omics technology provides a new perspective for analyzing the genetic basis of photosynthetic efficiency. The natural variation sites identified through techniques such as chlorophyll fluorescence imaging can guide molecular design breeding, optimize agronomic management thresholds, and maximize the "genotype environment" interaction effect (Van Bezouw et al., 2019). This multi-scale integration strategy provides a systematic solution for targeted improvement of potato photosynthetic performance. 7.2 Challenges in translating lab-based improvements to field conditions The core challenge facing research on improving potato photosynthetic efficiency is the transformation of laboratory genetic improvement results into field production systems. There are significant differences between the controlled environment of the laboratory and the actual growth conditions in the field. The latter involves dynamic fluctuations of multiple environmental factors such as light intensity, diurnal temperature changes, and
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